{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## SOG stats"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import netCDF4 as nc\n",
"import seaborn as sns\n",
"import matplotlib.colors as mcolors\n",
"import glob\n",
"import os\n",
"import xarray as xr\n",
"import datetime\n",
"from salishsea_tools import viz_tools, tidetools, geo_tools, gsw_calls\n",
"import pickle\n",
"from pandas.plotting import register_matplotlib_converters\n",
"register_matplotlib_converters()\n",
"from IPython.core.display import display, HTML\n",
"display(HTML(\"\"))\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
""
],
"text/plain": [
""
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import HTML\n",
"\n",
"HTML('''\n",
"\n",
"''')\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"pickle_in1 = open(\"/home/abhudia/Desktop/Wind speed/3points/winds_sog2015.pickle\",\"rb\")\n",
"pickle_in2 = open(\"/home/abhudia/Desktop/Wind speed/3points/winds_sog2016.pickle\",\"rb\")\n",
"pickle_in3 = open(\"/home/abhudia/Desktop/Wind speed/3points/winds_sog2017.pickle\",\"rb\")\n",
"pickle_in4 = open(\"/home/abhudia/Desktop/Wind speed/3points/winds_sog2018.pickle\",\"rb\")\n",
"example1 = pickle.load(pickle_in1)\n",
"example2 = pickle.load(pickle_in2)\n",
"example3 = pickle.load(pickle_in3)\n",
"example4 = pickle.load(pickle_in4)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"two = np.append(example1, example2)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"three = np.append(two, example3)\n",
"full = np.append(three, example4)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"wnd_avg = np.array([])\n",
"wnd_min = np.array([])\n",
"wnd_max = np.array([])\n",
"wnd_std = np.array([])\n",
"\n",
"for i in range(1450):\n",
" start = 24*i\n",
" end = start + 168\n",
" wnd_avg = np.append(wnd_avg, full[start:end].mean())\n",
" wnd_min = np.append(wnd_min, full[start:end].min())\n",
" wnd_max = np.append(wnd_max, full[start:end].max())\n",
" wnd_std = np.append(wnd_std, full[start:end].std())"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"pickle_in1 = open(\"/home/abhudia/Desktop/current speed/hourly/mag2015.pickle\",\"rb\")\n",
"pickle_in2 = open(\"/home/abhudia/Desktop/current speed/hourly/mag2016.pickle\",\"rb\")\n",
"pickle_in3 = open(\"/home/abhudia/Desktop/current speed/hourly/mag2017.pickle\",\"rb\")\n",
"pickle_in4 = open(\"/home/abhudia/Desktop/current speed/hourly/mag2018.pickle\",\"rb\")\n",
"example1 = pickle.load(pickle_in1)\n",
"example2 = pickle.load(pickle_in2)\n",
"example3 = pickle.load(pickle_in3)\n",
"example4 = pickle.load(pickle_in4)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(35064,)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"two = np.append(example1[:,274,242], example2[:,274,242])\n",
"three = np.append(two, example3[:,274,242])\n",
"fullc = np.append(three, example4[:,274,242])\n",
"fullc.shape"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"cur_avg = np.array([])\n",
"cur_min = np.array([])\n",
"cur_max = np.array([])\n",
"cur_std = np.array([])\n",
"\n",
"for i in range(1450):\n",
" start = 24*i\n",
" end = start + 168\n",
" cur_avg = np.append(cur_avg, fullc[start:end].mean())\n",
" cur_min = np.append(cur_min, fullc[start:end].min())\n",
" cur_max = np.append(cur_max, fullc[start:end].max())\n",
" cur_std = np.append(cur_std, fullc[start:end].std())"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"pickle_in1 = open(\"/home/abhudia/Desktop/salinity/3points/sog2015.pickle\",\"rb\")\n",
"pickle_in2 = open(\"/home/abhudia/Desktop/salinity/3points/sog2016.pickle\",\"rb\")\n",
"pickle_in3 = open(\"/home/abhudia/Desktop/salinity/3points/sog2017.pickle\",\"rb\")\n",
"pickle_in4 = open(\"/home/abhudia/Desktop/salinity/3points/sog2018.pickle\",\"rb\")\n",
"example1 = pickle.load(pickle_in1)\n",
"example2 = pickle.load(pickle_in2)\n",
"example3 = pickle.load(pickle_in3)\n",
"example4 = pickle.load(pickle_in4)\n",
"\n",
"two = np.append(example1, example2)\n",
"three = np.append(two, example3)\n",
"fulls = np.append(three, example4)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"sal_avg = np.array([])\n",
"sal_min = np.array([])\n",
"sal_max = np.array([])\n",
"sal_std = np.array([])\n",
"\n",
"for i in range(1450):\n",
" start = 24*i\n",
" end = start + 168\n",
" sal_avg = np.append(sal_avg, fulls[start:end].mean())\n",
" sal_min = np.append(sal_min, fulls[start:end].min())\n",
" sal_max = np.append(sal_max, fulls[start:end].max())\n",
" sal_std = np.append(sal_std, fulls[start:end].std())"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1450,)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dates = np.array([datetime.date(2015,1,1) + datetime.timedelta(i) for i in range(1450)])\n",
"dates.shape"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"dates2 = np.array([datetime.datetime(2015,1,1,0,30) + datetime.timedelta(hours = i) for i in range(35064)])\n",
"month_of_data = np.array([dates2[a].month for a in range(35064)])\n",
"monthly_sal_avg = np.array([])\n",
"monthly_cur_avg = np.array([])\n",
"monthly_wnd_avg = np.array([])\n",
"for a in range(1,13):\n",
" monthly_sal_avg = np.append(monthly_sal_avg, fulls[month_of_data==a].mean())\n",
" monthly_cur_avg = np.append(monthly_cur_avg, fullc[month_of_data==a].mean())\n",
" monthly_wnd_avg = np.append(monthly_wnd_avg, full[month_of_data==a].mean())"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
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